{"id":566,"date":"2017-06-27T11:17:10","date_gmt":"2017-06-27T10:17:10","guid":{"rendered":"http:\/\/babel.isa.uma.es\/kipr\/?p=566"},"modified":"2017-06-27T11:17:10","modified_gmt":"2017-06-27T10:17:10","slug":"modelling-hierarchical-stochastic-signals-i-e-decomposable-into-sub-signals-hierarchichally","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=566","title":{"rendered":"Modelling hierarchical stochastic signals (i.e., decomposable into sub-signals hierarchichally)"},"content":{"rendered":"<h4>Truyen Tran, Dinh Phung, Hung Bui, Svetha Venkatesh, <strong>Hierarchical semi-Markov conditional random fields for deep recursive sequential data,<\/strong> Artificial Intelligence, Volume 246, May 2017, Pages 53-85, ISSN 0004-3702, <a href=\"https:\/\/doi.org\/10.1016\/j.artint.2017.02.003\" target=\"_blank\">DOI: 10.1016\/j.artint.2017.02.003<\/a>.<\/h4>\n<blockquote><p>We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes. It is parameterised as a conditional log-linear model and has polynomial time algorithms for learning and inference. We derive algorithms for partially-supervised learning and constrained inference. We develop numerical scaling procedures that handle the overflow problem. We show that when depth is two, the HSCRF can be reduced to the semi-Markov conditional random fields. Finally, we demonstrate the HSCRF on two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. The HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Truyen Tran, Dinh Phung, Hung Bui, Svetha Venkatesh, Hierarchical semi-Markov conditional random fields for deep recursive sequential data, Artificial Intelligence, <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=566\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[37,41],"tags":[99,197,122,239],"class_list":["post-566","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","category-probability-and-statistics","tag-conditional-random-fields","tag-hierarchies-of-abstraction","tag-mdps","tag-semi-markov-processes"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/566"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=566"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/566\/revisions"}],"predecessor-version":[{"id":567,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/566\/revisions\/567"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=566"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=566"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=566"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}